R Notebook
install.packages("DT")library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ tibble 3.0.4 ✓ dplyr 1.0.2
✓ tidyr 1.1.2 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.0
✓ purrr 0.3.4
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks plotly::filter(), stats::filter()
x dplyr::lag() masks stats::lag()library(forecast)Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
This is forecast 8.13
Need help getting started? Try the online textbook FPP:
http://otexts.com/fpp2/library(caTools)
library(earth)Loading required package: Formula
Loading required package: plotmo
Loading required package: plotrix
Loading required package: TeachingDemos
Attaching package: ‘TeachingDemos’
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subplotlibrary(randomForest)randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
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alphalibrary(h2o)
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Your next step is to start H2O:
> h2o.init()
For H2O package documentation, ask for help:
> ??h2o
After starting H2O, you can use the Web UI at http://localhost:54321
For more information visit https://docs.h2o.ai
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accuracylibrary(caret)Loading required package: lattice
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library(gganimate)
library(gapminder)
library(gifski)
library(png)
library(shiny)
library(plotly)
library(shinydashboard)
library(DT)DATA
Mall_customers <- read.csv("Mall_Customers.csv", stringsAsFactors = T)
Mall_customers <- Mall_customers %>% rename(
Annual_Income = Annual.Income..k..,
Spending_Score = Spending.Score..1.100.)
Mall_customers <- Mall_customers %>%
select(Genre, Age, Annual_Income, Spending_Score)
Mall_customers <- Mall_customers %>% mutate(
Gender = if_else(Genre == "Male", true = 1, false = 0)
) %>%
select(-Genre)
Mall_customerssummary(Mall_customers) Age Annual_Income Spending_Score Gender
Min. :18.00 Min. : 15.00 Min. : 1.00 Min. :0.00
1st Qu.:28.75 1st Qu.: 41.50 1st Qu.:34.75 1st Qu.:0.00
Median :36.00 Median : 61.50 Median :50.00 Median :0.00
Mean :38.85 Mean : 60.56 Mean :50.20 Mean :0.44
3rd Qu.:49.00 3rd Qu.: 78.00 3rd Qu.:73.00 3rd Qu.:1.00
Max. :70.00 Max. :137.00 Max. :99.00 Max. :1.00 DATA SPLIT
set.seed(123)
sample = sample.split(Mall_customers$Gender, SplitRatio = .75)
train = subset(Mall_customers, sample == TRUE)
test = subset(Mall_customers, sample == FALSE)RAMDOM FOREST
# Random Forest Model
RFModel <- randomForest(Gender ~ ., data = train, mtry = 3, ntree = 64)The response has five or fewer unique values. Are you sure you want to do regression?RFModel
Call:
randomForest(formula = Gender ~ ., data = train, mtry = 3, ntree = 64)
Type of random forest: regression
Number of trees: 64
No. of variables tried at each split: 3
Mean of squared residuals: 0.2689081
% Var explained: -9.13plot(RFModel)
# Prediction
RFPrediction <- predict(RFModel, test, type = "class")
RFPrediction 2 3 11 13 15 19 24 29 32 34 35
0.29843750 0.59791667 0.51250000 0.48281250 0.26458333 0.15338542 0.48802083 0.14583333 0.29895833 0.57890625 0.33984375
39 44 45 48 54 56 61 63 73 76 77
0.14973958 0.29973958 0.17369792 0.09427083 0.46458333 0.12161458 0.50494792 0.70416667 0.63984375 0.39713542 0.21588542
78 82 84 85 87 95 106 107 121 128 137
0.21354167 0.22213542 0.18385417 0.43671875 0.33723958 0.10729167 0.37187500 0.93046875 0.28020833 0.79479167 0.82239583
138 149 150 152 153 157 159 160 162 166 182
0.12473958 0.35130208 0.36067708 0.41822917 0.71067708 0.83203125 0.84140625 0.08463542 0.05312500 0.49843750 0.32656250
184 188 189 191 199 200
0.42864583 0.57578125 0.27708333 0.26223958 0.51041667 0.53385417 # Generate Confusion Matrix
RFconfusionMatrix <- table(RFPrediction,
test$Gender,
dnn = c("Prediction", "Actual"))
# Calculate Accuracy
RFaccuracy <- round(sum(diag(RFconfusionMatrix))/ sum(RFconfusionMatrix), digits = 4)
cat("Random Forest Accuracy:", RFaccuracy)Random Forest Accuracy: 0.02# MSE
#Metrics::mse(test, RFPrediction)#SVM
# Support Vector Machine Model
SVMmodel <- ksvm(Gender ~ .,
data = train,
kernel = "vanilladot") Setting default kernel parameters SVMmodelSupport Vector Machine object of class "ksvm"
SV type: eps-svr (regression)
parameter : epsilon = 0.1 cost C = 1
Linear (vanilla) kernel function.
Number of Support Vectors : 135
Objective Function Value : -119.3168
Training error : 1.606914 # Prediction
SVMpred <- predict(SVMmodel, test)
# Generate Confusion Matrix
SVMconfusionMatrix <- table(SVMpred,
test$Gender,
dnn = c("Prediction", "Actual"))
# Calculate Accuracy
SVMaccuracy <- round(sum(diag(SVMconfusionMatrix))/ sum(SVMconfusionMatrix), digits = 4)
cat("Support Vector Machine Accuracy:", SVMaccuracy)Support Vector Machine Accuracy: 0# MSE
#Metrics::mse(test, SVMpred)NEURAL NETWORKS
# Neural Networks Model
NNModel <- neuralnet(Gender ~ .,
data = train)
NNModel$call
neuralnet(formula = Gender ~ ., data = train)
$response
Gender
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$covariate
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attr(,"type")
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function (x)
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}
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attr(,"type")
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93 1.631930e-14 1.183238e-14 3.631364e-14
94 1.746078e-12 1.266001e-12 3.885366e-12
96 2.931754e-13 2.125681e-13 6.523728e-13
97 4.049054e-14 2.935783e-14 9.009939e-14
98 3.733770e-13 2.707186e-13 8.308370e-13
99 1.941135e-13 1.407429e-13 4.319407e-13
100 1.540373e-12 1.116854e-12 3.427632e-12
101 1.610194e-11 1.167478e-11 3.582998e-11
102 1.571868e-14 1.139689e-14 3.497714e-14
103 1.320474e-17 9.574153e-18 2.938314e-17
104 5.419335e-14 3.929312e-14 1.205908e-13
105 8.080532e-16 5.858824e-16 1.798077e-15
108 1.270926e-14 9.214904e-15 2.828060e-14
109 3.747782e-15 2.717345e-15 8.339550e-15
110 8.184276e-16 5.934044e-16 1.821162e-15
111 2.192301e-16 1.589537e-16 4.878300e-16
112 2.235756e-13 1.621044e-13 4.974995e-13
113 7.155446e-13 5.188086e-13 1.592227e-12
114 3.853902e-12 2.794288e-12 8.575689e-12
115 1.921166e-12 1.392950e-12 4.274972e-12
116 7.743065e-13 5.614142e-13 1.722984e-12
117 6.773527e-15 4.911175e-15 1.507243e-14
118 1.847459e-16 1.339508e-16 4.110958e-16
119 3.932887e-14 2.851556e-14 8.751445e-14
120 2.578762e-16 1.869742e-16 5.738252e-16
122 1.045638e-12 7.581446e-13 2.326751e-12
123 7.402354e-16 5.367108e-16 1.647169e-15
124 4.214552e-21 3.055779e-21 9.378205e-21
125 5.252239e-10 3.808158e-10 1.168726e-09
126 2.554018e-18 1.851801e-18 5.683190e-18
127 1.790411e-12 1.298145e-12 3.984015e-12
129 9.148563e-10 6.633205e-10 2.035735e-09
130 1.479490e-18 1.072711e-18 3.292156e-18
131 1.420669e-08 1.030062e-08 3.161268e-08
132 1.252288e-18 9.079766e-19 2.786586e-18
133 4.622948e-11 3.351888e-11 1.028697e-10
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135 4.436047e-06 3.216375e-06 9.871076e-06
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139 7.266003e-07 5.268247e-07 1.616828e-06
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143 1.866225e-12 1.353114e-12 4.152717e-12
144 2.562108e-20 1.857667e-20 5.701192e-20
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146 1.082703e-21 7.850188e-22 2.409228e-21
147 2.598916e-13 1.884355e-13 5.783098e-13
148 2.822824e-18 2.046701e-18 6.281337e-18
151 6.104509e-10 4.426100e-10 1.358373e-09
154 4.380181e-19 3.175869e-19 9.746762e-19
155 4.540888e-10 3.292390e-10 1.010437e-09
156 2.204925e-20 1.598691e-20 4.906392e-20
158 7.916114e-19 5.739612e-19 1.761491e-18
161 7.792033e-14 5.649646e-14 1.733880e-13
163 1.992540e-06 1.444700e-06 4.433793e-06
164 1.785561e-21 1.294628e-21 3.973223e-21
165 2.892001e-12 2.096858e-12 6.435271e-12
167 9.009379e-11 6.532289e-11 2.004764e-10
168 3.328250e-22 2.413162e-22 7.406010e-22
169 1.617294e-11 1.172626e-11 3.598797e-11
170 4.989174e-17 3.617422e-17 1.110189e-16
171 1.495771e-09 1.084515e-09 3.328385e-09
172 1.132913e-18 8.214233e-19 2.520953e-18
173 8.868768e-09 6.430338e-09 1.973475e-08
174 5.443250e-22 3.946651e-22 1.211230e-21
175 1.792543e-10 1.299691e-10 3.988759e-10
176 1.214895e-20 8.808646e-21 2.703379e-20
177 3.138900e-11 2.275873e-11 6.984668e-11
178 1.098593e-17 7.965396e-18 2.444585e-17
179 2.103744e-11 1.525328e-11 4.681242e-11
180 6.539009e-22 4.741136e-22 1.455058e-21
181 6.394016e-13 4.636008e-13 1.422794e-12
183 6.929089e-11 5.023966e-11 1.541858e-10
185 1.919942e-14 1.392062e-14 4.272248e-14
186 5.428745e-23 3.936135e-23 1.208002e-22
187 4.506245e-13 3.267272e-13 1.002728e-12
190 1.056128e-21 7.657503e-22 2.350093e-21
192 7.785758e-19 5.645097e-19 1.732484e-18
193 1.325374e-09 9.609678e-10 2.949216e-09
194 2.438772e-23 1.768242e-23 5.426747e-23
195 2.832384e-12 2.053632e-12 6.302609e-12
196 1.480340e-21 1.073326e-21 3.294046e-21
197 2.231031e-14 1.617618e-14 4.964481e-14
198 7.554349e-21 5.477313e-21 1.680991e-20
$startweights
$startweights[[1]]
$startweights[[1]][[1]]
[,1]
[1,] 0.89214945
[2,] -0.83512559
[3,] 0.53658644
[4,] 0.04892574
$startweights[[1]][[2]]
[,1]
[1,] -0.4243741
[2,] -1.5065810
$result.matrix
[,1]
error 18.479874846
reached.threshold 0.004433007
steps 35.000000000
Intercept.to.1layhid1 1.385749453
Age.to.1layhid1 -0.166725588
Annual_Income.to.1layhid1 -0.120885097
Spending_Score.to.1layhid1 -0.370997144
Intercept.to.Gender 0.440028940
1layhid1.to.Gender -2.000180983
attr(,"class")
[1] "nn"# Visualization
NNModelPlot <- plot(NNModel)
NNModelPlotNULL# Prediction
NNPrediction <- predict(NNModel, test)
# Generate Confusion Matrix
NNconfusionMatrix <- table(NNPrediction,
test$Gender,
dnn = c("Prediction", "Actual"))
# Calculate Accuracy
NNaccuracy <- round(sum(diag(NNconfusionMatrix))/ sum(NNconfusionMatrix), digits = 4)
cat("Neural Network Model Accuracy:", NNaccuracy)Neural Network Model Accuracy: 0.04# MSE
#Metrics::mse(test, NNPrediction)DEEP LEARNING NEURAL NETWORKS
# Neural Networks Model
DNNModel <- neuralnet(Gender ~ .,
data = train,
hidden = 4)
DNNModel$call
neuralnet(formula = Gender ~ ., data = train, hidden = 4)
$response
Gender
1 1
4 0
5 0
6 0
7 0
8 0
9 1
10 0
12 0
14 0
16 1
17 0
18 1
20 0
21 1
22 1
23 0
25 0
26 1
27 0
28 1
30 0
31 1
33 1
36 0
37 0
38 0
40 0
41 0
42 1
43 1
46 0
47 0
49 0
50 0
51 0
52 1
53 0
55 0
57 0
58 1
59 0
60 1
62 1
64 0
65 1
66 1
67 0
68 0
69 1
70 0
71 1
72 0
74 0
75 1
79 0
80 0
81 1
83 1
86 1
88 0
89 0
90 0
91 0
92 1
93 1
94 0
96 1
97 0
98 0
99 1
100 1
101 0
102 0
103 1
104 1
105 1
108 1
109 1
110 1
111 1
112 0
113 0
114 1
115 0
116 0
117 0
118 0
119 0
120 0
122 0
123 0
124 1
125 0
126 0
127 1
129 1
130 1
131 1
132 1
133 0
134 0
135 1
136 0
139 1
140 0
141 0
142 1
143 0
144 0
145 1
146 1
147 1
148 0
151 1
154 0
155 0
156 0
158 0
161 0
163 1
164 0
165 1
167 1
168 0
169 0
170 1
171 1
172 1
173 1
174 1
175 0
176 0
177 1
178 1
179 1
180 1
181 0
183 1
185 0
186 1
187 0
190 0
192 0
193 1
194 0
195 0
196 0
197 0
198 1
$covariate
Age Annual_Income Spending_Score
1 19 15 39
4 23 16 77
5 31 17 40
6 22 17 76
7 35 18 6
8 23 18 94
9 64 19 3
10 30 19 72
12 35 19 99
14 24 20 77
16 22 20 79
17 35 21 35
18 20 21 66
20 35 23 98
21 35 24 35
22 25 24 73
23 46 25 5
25 54 28 14
26 29 28 82
27 45 28 32
28 35 28 61
30 23 29 87
31 60 30 4
33 53 33 4
36 21 33 81
37 42 34 17
38 30 34 73
40 20 37 75
41 65 38 35
42 24 38 92
43 48 39 36
46 24 39 65
47 50 40 55
49 29 40 42
50 31 40 42
51 49 42 52
52 33 42 60
53 31 43 54
55 50 43 45
57 51 44 50
58 69 44 46
59 27 46 51
60 53 46 46
62 19 46 55
64 54 47 59
65 63 48 51
66 18 48 59
67 43 48 50
68 68 48 48
69 19 48 59
70 32 48 47
71 70 49 55
72 47 49 42
74 60 50 56
75 59 54 47
79 23 54 52
80 49 54 42
81 57 54 51
83 67 54 41
86 48 54 46
88 22 57 55
89 34 58 60
90 50 58 46
91 68 59 55
92 18 59 41
93 48 60 49
94 40 60 40
96 24 60 52
97 47 60 47
98 27 60 50
99 48 61 42
100 20 61 49
101 23 62 41
102 49 62 48
103 67 62 59
104 26 62 55
105 49 62 56
108 54 63 46
109 68 63 43
110 66 63 48
111 65 63 52
112 19 63 54
113 38 64 42
114 19 64 46
115 18 65 48
116 19 65 50
117 63 65 43
118 49 65 59
119 51 67 43
120 50 67 57
122 38 67 40
123 40 69 58
124 39 69 91
125 23 70 29
126 31 70 77
127 43 71 35
129 59 71 11
130 38 71 75
131 47 71 9
132 39 71 75
133 25 72 34
134 31 72 71
135 20 73 5
136 29 73 88
139 19 74 10
140 35 74 72
141 57 75 5
142 32 75 93
143 28 76 40
144 32 76 87
145 25 77 12
146 28 77 97
147 48 77 36
148 32 77 74
151 43 78 17
154 38 78 76
155 47 78 16
156 27 78 89
158 30 78 78
161 56 79 35
163 19 81 5
164 31 81 93
165 50 85 26
167 42 86 20
168 33 86 95
169 36 87 27
170 32 87 63
171 40 87 13
172 28 87 75
173 36 87 10
174 36 87 92
175 52 88 13
176 30 88 86
177 58 88 15
178 27 88 69
179 59 93 14
180 35 93 90
181 37 97 32
183 46 98 15
185 41 99 39
186 30 99 97
187 54 101 24
190 36 103 85
192 32 103 69
193 33 113 8
194 38 113 91
195 47 120 16
196 35 120 79
197 45 126 28
198 32 126 74
$model.list
$model.list$response
[1] "Gender"
$model.list$variables
[1] "Age" "Annual_Income" "Spending_Score"
$err.fct
function (x, y)
{
1/2 * (y - x)^2
}
<bytecode: 0x7f8e78d00c38>
<environment: 0x7f8e89b4a058>
attr(,"type")
[1] "sse"
$act.fct
function (x)
{
1/(1 + exp(-x))
}
<bytecode: 0x7f8e78cf6710>
<environment: 0x7f8e89b49bc0>
attr(,"type")
[1] "logistic"
$linear.output
[1] TRUE
$data
Age Annual_Income Spending_Score Gender
1 19 15 39 1
4 23 16 77 0
5 31 17 40 0
6 22 17 76 0
7 35 18 6 0
8 23 18 94 0
9 64 19 3 1
10 30 19 72 0
12 35 19 99 0
14 24 20 77 0
16 22 20 79 1
17 35 21 35 0
18 20 21 66 1
20 35 23 98 0
21 35 24 35 1
22 25 24 73 1
23 46 25 5 0
25 54 28 14 0
26 29 28 82 1
27 45 28 32 0
28 35 28 61 1
30 23 29 87 0
31 60 30 4 1
33 53 33 4 1
36 21 33 81 0
37 42 34 17 0
38 30 34 73 0
40 20 37 75 0
41 65 38 35 0
42 24 38 92 1
43 48 39 36 1
46 24 39 65 0
47 50 40 55 0
49 29 40 42 0
50 31 40 42 0
51 49 42 52 0
52 33 42 60 1
53 31 43 54 0
55 50 43 45 0
57 51 44 50 0
58 69 44 46 1
59 27 46 51 0
60 53 46 46 1
62 19 46 55 1
64 54 47 59 0
65 63 48 51 1
66 18 48 59 1
67 43 48 50 0
68 68 48 48 0
69 19 48 59 1
70 32 48 47 0
71 70 49 55 1
72 47 49 42 0
74 60 50 56 0
75 59 54 47 1
79 23 54 52 0
80 49 54 42 0
81 57 54 51 1
83 67 54 41 1
86 48 54 46 1
88 22 57 55 0
89 34 58 60 0
90 50 58 46 0
91 68 59 55 0
92 18 59 41 1
93 48 60 49 1
94 40 60 40 0
96 24 60 52 1
97 47 60 47 0
98 27 60 50 0
99 48 61 42 1
100 20 61 49 1
101 23 62 41 0
102 49 62 48 0
103 67 62 59 1
104 26 62 55 1
105 49 62 56 1
108 54 63 46 1
109 68 63 43 1
110 66 63 48 1
111 65 63 52 1
112 19 63 54 0
113 38 64 42 0
114 19 64 46 1
115 18 65 48 0
116 19 65 50 0
117 63 65 43 0
118 49 65 59 0
119 51 67 43 0
120 50 67 57 0
122 38 67 40 0
123 40 69 58 0
124 39 69 91 1
125 23 70 29 0
126 31 70 77 0
127 43 71 35 1
129 59 71 11 1
130 38 71 75 1
131 47 71 9 1
132 39 71 75 1
133 25 72 34 0
134 31 72 71 0
135 20 73 5 1
136 29 73 88 0
139 19 74 10 1
140 35 74 72 0
141 57 75 5 0
142 32 75 93 1
143 28 76 40 0
144 32 76 87 0
145 25 77 12 1
146 28 77 97 1
147 48 77 36 1
148 32 77 74 0
151 43 78 17 1
154 38 78 76 0
155 47 78 16 0
156 27 78 89 0
158 30 78 78 0
161 56 79 35 0
163 19 81 5 1
164 31 81 93 0
165 50 85 26 1
167 42 86 20 1
168 33 86 95 0
169 36 87 27 0
170 32 87 63 1
171 40 87 13 1
172 28 87 75 1
173 36 87 10 1
174 36 87 92 1
175 52 88 13 0
176 30 88 86 0
177 58 88 15 1
178 27 88 69 1
179 59 93 14 1
180 35 93 90 1
181 37 97 32 0
183 46 98 15 1
185 41 99 39 0
186 30 99 97 1
187 54 101 24 0
190 36 103 85 0
192 32 103 69 0
193 33 113 8 1
194 38 113 91 0
195 47 120 16 0
196 35 120 79 0
197 45 126 28 0
198 32 126 74 1
$exclude
NULL
$net.result
$net.result[[1]]
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$weights
$weights[[1]]
$weights[[1]][[1]]
[,1] [,2] [,3] [,4]
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[2,] 1.01488201 0.06800324 0.10120237 2.36576666
[3,] 1.05156374 -0.04655805 -0.05647392 -0.09091469
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$weights[[1]][[2]]
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[3,] 3.2063151
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$generalized.weights
$generalized.weights[[1]]
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$startweights
$startweights[[1]]
$startweights[[1]][[1]]
[,1] [,2] [,3] [,4]
[1,] 0.4437095 -0.009397413 -0.4199274 -0.71938925
[2,] 0.8148820 0.031137453 -0.7091934 -0.04007548
[3,] 0.8515637 -1.429511768 0.3394200 -0.38164388
[4,] -0.1312506 -0.627410443 -2.4553174 1.15547632
$startweights[[1]][[2]]
[,1]
[1,] -0.34435803
[2,] 1.48338742
[3,] 0.08828987
[4,] 0.74028344
[5,] -1.06531448
$result.matrix
[,1]
error 1.615756e+01
reached.threshold 9.354496e-03
steps 2.485400e+04
Intercept.to.1layhid1 6.437095e-01
Age.to.1layhid1 1.014882e+00
Annual_Income.to.1layhid1 1.051564e+00
Spending_Score.to.1layhid1 6.874939e-02
Intercept.to.1layhid2 1.199907e+01
Age.to.1layhid2 6.800324e-02
Annual_Income.to.1layhid2 -4.655805e-02
Spending_Score.to.1layhid2 -2.773476e+00
Intercept.to.1layhid3 6.393853e+00
Age.to.1layhid3 1.012024e-01
Annual_Income.to.1layhid3 -5.647392e-02
Spending_Score.to.1layhid3 -1.579646e+00
Intercept.to.1layhid4 -1.645882e+02
Age.to.1layhid4 2.365767e+00
Annual_Income.to.1layhid4 -9.091469e-02
Spending_Score.to.1layhid4 4.156184e+00
Intercept.to.Gender -4.072916e-01
1layhid1.to.Gender 1.420454e+00
1layhid2.to.Gender 3.206315e+00
1layhid3.to.Gender -3.164347e+00
1layhid4.to.Gender -6.159011e-01
attr(,"class")
[1] "nn"# Visualization
plot(DNNModel)
# Prediction
DNNPrediction <- predict(DNNModel, test)
# Generate Confusion Matrix
DNNconfusionMatrix <- table(DNNPrediction,
test$Gender,
dnn = c("Prediction", "Actual"))
# Calculate Accuracy
DNNaccuracy <- round(sum(diag(DNNconfusionMatrix))/ sum(DNNconfusionMatrix), digits = 4)
cat("Deep Neural Network Model Accuracy:", DNNaccuracy)Deep Neural Network Model Accuracy: 0# MSE
#Metrics::mse(test, NNPrediction)
AUTO ML
localH2O = h2o.init() Connection successful!
R is connected to the H2O cluster:
H2O cluster uptime: 3 hours 4 minutes
H2O cluster timezone: America/New_York
H2O data parsing timezone: UTC
H2O cluster version: 3.32.0.1
H2O cluster version age: 6 months and 24 days !!!
H2O cluster name: H2O_started_from_R_xiochirinos_nah310
H2O cluster total nodes: 1
H2O cluster total memory: 1.38 GB
H2O cluster total cores: 8
H2O cluster allowed cores: 8
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
H2O API Extensions: Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
R Version: R version 4.0.3 (2020-10-10)
Your H2O cluster version is too old (6 months and 24 days)!
Please download and install the latest version from http://h2o.ai/download/# Convert the data frame to an H2O Data Frame
autoMall_customers <- as.h2o(Mall_customers)
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|====================================================================================================================| 100%# Sample Data
autoSplit <- h2o.splitFrame(data = autoMall_customers, ratios = c(.75))
AMLtrain <- autoSplit[[1]]
AMLtestValidation <- autoSplit[[2]]
testValidationSplit <- h2o.splitFrame(data = AMLtestValidation, ratios = c(.75))
AMLtest <- testValidationSplit[[1]]
AMLvalidation <- testValidationSplit[[2]]
# AutoML
autoMLModel <- h2o.automl(y = "Gender",
x = c("Age", "Annual_Income", "Spending_Score"),
training_frame = AMLtrain,
validation_frame = AMLvalidation,
balance_classes = TRUE,
max_runtime_secs = 60,
seed = 1234)
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16:01:53.249: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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16:02:02.317: Skipping training of model GBM_5_AutoML_20210503_160153 due to exception: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for GBM model: GBM_5_AutoML_20210503_160153. Details: ERRR on field: _min_rows: The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 148.0.
16:02:03.326: DeepLearning_1_AutoML_20210503_160153 [DeepLearning def_1] failed: water.exceptions.H2OModelBuilderIllegalArgumentException: Illegal argument(s) for DeepLearning model: DeepLearning_1_AutoML_20210503_160153_cv_1. Details: ERRR on field: _balance_classes: balance_classes requires classification.
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|====================================================================================================================| 100%# Prediction
AMLprediction = h2o.predict(object = autoMLModel, newdata = AMLtest)
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|====================================================================================================================| 100%AutoMLtable <- as.data.frame(h2o.get_leaderboard(object = autoMLModel, extra_columns = 'ALL'))
AutoMLtable
# Performance
print(h2o.performance(autoMLModel@leader, AMLtest))H2ORegressionMetrics: stackedensemble
MSE: 0.2491534
RMSE: 0.4991527
MAE: 0.4936293
RMSLE: 0.3473586
Mean Residual Deviance : 0.2491534# MSE
#Metrics::mse(AMLtest, AMLprediction)PLOTS
Mall_customers_Plot1 <- read.csv("Mall_Customers.csv")
Mall_customers_Plot1 <- Mall_customers_Plot1 %>% rename(
Annual_Income = Annual.Income..k..,
Spending_Score = Spending.Score..1.100.,
Gender = Genre)
Mall_customers_Plot1 <- Mall_customers_Plot1 %>%
select(Gender, Age, Annual_Income, Spending_Score)
Mall_customers_Plot1$Gender <- as.factor(Mall_customers_Plot1$Gender)
Mall_customers_Plot1NAANIMATED PLOT
AnnualIncome_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Annual_Income, y = Spending_Score, color = Gender)) +
geom_line(alpha = 0.7) +
theme_minimal() +
theme(legend.position="none") +
theme(axis.text.x = element_text(face = "bold", size = 10)) +
theme(axis.text.y = element_text(face = "bold", size = 10)) +
theme(axis.title.x = element_text(size = 15, face = "bold"),
axis.title.y = element_text(size = 15, face = "bold")) +
labs(title = "Annual Income vs. Spending Score",
x = 'Annual Income',
y = 'Spending Score') +
transition_reveal(Annual_Income)
AnnualIncome_SpendingScore
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Encoding to gif... done!PLOTLY PLOTS
Age_SpendingScore <- ggplot(Mall_customers_Plot, aes(x = Age, y = Spending_Score, fill = Gender)) +
geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
theme_minimal() +
theme(legend.position="none") +
theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
theme(axis.text.y = element_text(face = "bold", size = 10)) +
theme(axis.title.x = element_text(size = 15, face = "bold"),
axis.title.y = element_text(size = 15, face = "bold")) +
labs(x = 'Age',
y = 'Spending Score')
ggplotly(Age_SpendingScore)Age_AnnualIncome <- ggplot(Mall_customers_Plot1, aes(x = Age, y = Annual_Income, fill = Gender)) +
geom_bar(alpha = 0.7, stat = "identity", position = "dodge") +
theme_minimal() +
theme(legend.position="none") +
theme(axis.text.x = element_text(face = "bold", size = 10, angle = 90)) +
theme(axis.text.y = element_text(face = "bold", size = 10)) +
theme(axis.title.x = element_text(size = 15, face = "bold"),
axis.title.y = element_text(size = 15, face = "bold")) +
labs(x = 'Age',
y = 'Annual Income')
ggplotly(Age_AnnualIncome)rsconnect::setAccountInfo(name='xiochirinos',
token='0F58DAD4CDCAC9786C37995CD1527BE4',
secret='mwRCIHucKohwJ5R6Q5RLUAQkYNL0bX1LtiHn4boh')library(rsconnect)
Attaching package: ‘rsconnect’
The following object is masked from ‘package:shiny’:
serverInforsconnect::deployApp('~/Desktop/MS_BIG_DATA/DATA VISUALIZATION/Final Project/Final_Project')Preparing to deploy application...DONE
Uploading bundle for application: 4077877...DONE
Deploying bundle: 4556395 for application: 4077877 ...
Waiting for task: 924636169
building: Parsing manifest
building: Building image: 5234364
building: Fetching packages
building: Installing packages
building: Installing files
building: Pushing image: 5234364
deploying: Starting instances
rollforward: Activating new instances
terminating: Stopping old instances
Application successfully deployed to https://xiochirinos.shinyapps.io/final_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SAnQW5udWFsIEluY29tZScpCgpnZ3Bsb3RseShBZ2VfQW5udWFsSW5jb21lKQpgYGAKCgpgYGB7cn0KcnNjb25uZWN0OjpzZXRBY2NvdW50SW5mbyhuYW1lPSd4aW9jaGlyaW5vcycsCgkJCSAgdG9rZW49JzBGNThEQUQ0Q0RDQUM5Nzg2QzM3OTk1Q0QxNTI3QkU0JywKCQkJICBzZWNyZXQ9J213UkNJSHVjS29od0o1UjZRNVJMVUFRa1lOTDBiWDFMdGlIbjRib2gnKQpgYGAKCmBgYHtyfQpsaWJyYXJ5KHJzY29ubmVjdCkKcnNjb25uZWN0OjpkZXBsb3lBcHAoJ34vRGVza3RvcC9NU19CSUdfREFUQS9EQVRBIFZJU1VBTElaQVRJT04vRmluYWwgUHJvamVjdC9GaW5hbF9Qcm9qZWN0JykKYGBgCg==